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The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU. [ABSTRACT FROM AUTHOR]

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments. [ABSTRACT FROM AUTHOR]

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.

Abstract The study and the detection of possible network attacks are essential for wireless networks, in particular for mobile cognitive radio networks due to its characteristics such as the dynamic spectrum allocation and constant frequency hopping. The primary user emulation attack is one of the most significant attacks in cognitive radio, because it hazards the complete cognitive cycle. The techniques used for the detection of primary user emulation found in the literature are based on a fixed attacker location. However, in a mobile environment, the attacker usually has dynamic locations and this compromises the current applied security techniques and generates inefficient attack detection. Therefore, our work proposes a novel technique using cross-layer design for the detection of primary user emulation with mobility. This attack detection technique was tested with experiments using software-defined radio equipment and mobile phones at indoor scenarios with dynamic locations and with a mobile phone base station built up also with software-defined radio. The obtained results show that the combination of the three utilized techniques, energy detection, motion estimation, and application information analysis, are able to optimize the detection with around 100% of effectiveness for the primary user emulation attack with dynamic location. The proposed technique shows that the energy detection time is around 100 ms and for the processing time of the information analysis in the mobile phone is about 30 s. This result shows a practical and effective approach to detect primary emulation attacks. The proposed technique, to the best of the authors’ knowledge, has not been presented before in the literature with experiments neither with mobility conditions of the attacker as presented in our proposed work.

This paper presents the results of the characterization of the attack known as the "primary user emulation" in mobile cognitive radio networks performing the implementation and testing. The tools and their configuration to carry out the attack are presented and their effects on the network are analyzed. The results show how to generate the attack with a software-defined radio equipment (SDR) using GNU-Radio and OpenBTS. The effects of the possible configurations of the attack on the network are shown, the malicious type generates constant interference on the primary or cognitive network, the selfish type allows to imitate a licensed or primary user generating interference to the primary network and inability to access the Cognitive Network while active. If the emulator's power level is fixed, the services it provides are stable. If the power is variable the services suffer intermittency. Primary user emulation is the attack that most affects the cognitive radio network so its effects are analyzed in order to propose ways of detecting or applying countermeasures.

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization's (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This approach is based on a multi-objective particle swarm optimization (MOPSO) algorithm for PU/PUE position detection. It minimizes concurrently the probability of detection error related to the received signal strength (RSS)/trilateration and the SNR, with the main objective of finding all optimized positions for the mobile anchor nodes and obtains the most accurate PUE attack detection. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a flexible wireless network that can solve the scarcity and underutilization problem of the spectrum by permitting unlicensed users to access licensed bands. The dynamic nature of CR makes it more vulnerable in terms of security. This paper's emphasis is on the primary user emulation attack, which poses a severe threat to the spectrum sensing operation of CR. In this attack, a malicious user imitates the signal characteristics of a licensed user (primary user) to disguise its true identity. Although many survey papers enhance our knowledge of cognitive radio security, this paper is an attempt to culminate new findings with the old ones to keep up the pace of the research community. Finally, the paper summarizes with some recommendations and future strategies pertinent to energy-efficient and flexible security methods for next-generation wireless systems. [ABSTRACT FROM AUTHOR]

We propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. FD communication improves the spectrum awareness capability of secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. Meanwhile, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification (SSDF) attacks also degrades the sensing performance. To alleviate the influence of interference and attacks, we design an EML framework that provides robust and accurate fusion performance. In such a context, we analyse the spectrum waste probability, collision probability and secondary throughput in both FD Listen-Before-Talk and Listen-And-Talk protocols. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of SSDF SUs, only at the cost of slightly higher inference time. [ABSTRACT FROM AUTHOR]

The study and the detection of possible network attacks are essential for wireless networks, in particular for mobile cognitive radio networks due to its characteristics such as the dynamic spectrum allocation and constant frequency hopping. The primary user emulation attack is one of the most significant attacks in cognitive radio, because it hazards the complete cognitive cycle. The techniques used for the detection of primary user emulation found in the literature are based on a fixed attacker location. However, in a mobile environment, the attacker usually has dynamic locations and this compromises the current applied security techniques and generates inefficient attack detection. Therefore, our work proposes a novel technique using cross-layer design for the detection of primary user emulation with mobility. This attack detection technique was tested with experiments using software-defined radio equipment and mobile phones at indoor scenarios with dynamic locations and with a mobile phone base station built up also with software-defined radio. The obtained results show that the combination of the three utilized techniques, energy detection, motion estimation, and application information analysis, are able to optimize the detection with around 100% of effectiveness for the primary user emulation attack with dynamic location. The proposed technique shows that the energy detection time is around 100 ms and for the processing time of the information analysis in the mobile phone is about 30 s. This result shows a practical and effective approach to detect primary emulation attacks. The proposed technique, to the best of the authors' knowledge, has not been presented before in the literature with experiments neither with mobility conditions of the attacker as presented in our proposed work. [ABSTRACT FROM AUTHOR]

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In this paper, we propose a novel primary user emulation (PUE) detection approach which employs a distributed sensor network, where each sensor node operates as an independent PUE detector. Distributed nodes collaborate in order to obtain the final detection results for the whole network. A voting algorithm is used to improve the performance of energy detection, while the classification is conducted by the nearest node in order to improve the efficiency of the detector. As a result of voting, if a potential primary user exists, then the features of the unknown user is compared with entries from the database in order to obtain a solid detection match. An artificial neural network (ANN) is used for the classification of an unknown user. To assess the accuracy of the detection result, we implement a reliability check at the output of ANN. The proposed algorithm is validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform. The experiment results show that the distributed network detector detects the PUE 180-200%, depending on the number of primary users, faster than single node detector. [ABSTRACT FROM AUTHOR]

Concerning the effect of cognitive radio networks( CRN) on safety and performance to authorized and unauthorized users,this paper proposed a multiple criteria analysis method on the basic of flexible safety framework. This method applied normalized weighted additive utility function( NWAUF) and non-parametric Bayesian inference to calculate the possibility of the eventual presence on a network attack. It divided the instantiation into two phases, the first phase was the attack analysis of multiple-standard primary user emulation (PUE) in which the standards was designed according to different criteria and requirements. And when the network needed to change or being attacked, it allowed the standards to be added or deleted flexibly. The second phase was the synergia of CRN nodes,which was the exchange between the initial probabilities of adjacent nodes. When the initial probability was calculated, this phase begined and the probability was shared with the neighbors. The simulations on real tracking confirm that the proposed method can significantly improve the performance of spectrum sensing. Even if the number of neighbors is few,the proposed method can still obtain similar detecting proportion with a plurality of adjacent nodes. [ABSTRACT FROM AUTHOR]

To timely and accurately grasp the attack situation over a wide range when a malicious user emulates the primary user and attacks the channel, and to display correctly spectrum situation, a situation awareness scheme for the interference situation of malicious users was proposed. Decision-makers can know well the attack situation over a wide range, by detecting the primary user emulation attacker's energy and location to build the interference situation, and drew the interference into a situation map. The proposed approach can separate the energy and position of the primary users and malicious users, so as to fully exploit the potential of spectrum holes to improve the accuracy of the spectrum map, and improve safety and system capacity of the channels.

This paper considers malicious user detection and energy harvesting in orthogonal frequency division multiplexing-based cognitive radio networks under primary user emulation attack (PUEA). The digital TV (DTV) model adopted is the second generation terrestrial digital television standard (DVB-T2). In this paper, first, we propose an efficient advanced encryption standard (AES)-based DTV scheme, where the existing reference sequence used to generate the P2 pilot symbols in the DVB-T2 frames is encrypted using the AES algorithm to facilitate accurate primary user and malicious user detection. With the proposed scheme, we can detect PUEA accurately over all subcarriers or subbands where the P2 symbols present. Second, we come up with an effective communication scheme for the secondary users (SUs) under PUEA by exploiting the energy harvesting techniques. Optimal power splitting is considered for sum-rate maximization. As the optimal solution relies on multidimensional exhaustive search, we propose an effective suboptimal solution with much lower computational complexity. It is observed that the sum-rate of the SU network can be improved significantly with the energy harvesting technique. Third, we evaluate the worst case PUEA interference in terms of minimizing the sum-rate for the SUs. We show that for practical systems, as channel state information among the SUs is not available to the malicious user, the worst jamming for the SUs is when the malicious user performs equal power allocation over all the white space subcarriers. Simulation results are provided to illustrate the proposed approaches. [ABSTRACT FROM PUBLISHER]

Abstract: Primary user emulation (PUE) attacks on cognitive radio networks pose a serious threat to the deployment of this technique. Previous approaches usually depend on individual or combined received signal strength (RSS) measurements to detect emulators. In this paper, we propose a new mechanism based on physical layer network coding to detect the emulators. When two signal sequences interfere at the receiver, the starting point of collision is determined by the distances among the receiver and the senders. Using the signal interference results at multiple receivers and the positions of reference senders, we can determine the position of the ‘claimed’ primary user. We can then compare this localization result with the known position of the primary user to detect the PUE attack. We design a PUE detection mechanism for wireless networks with trustworthy reference senders. We analyze the overhead of the proposed approach and study its detection accuracy through simulation. [Copyright &y& Elsevier]

To solve the threat to the primary user emulation in cognitive radio networks, secure localization is proposed as a security scheme based on three-dimensional positioning of the signal source location. Chan Location Algorithm is expanded into three-dimensional form and the signal propagation time is used to estimate the source location with the weighted least square iteratively. The simulation results show that this method has good performance and can effectively solve primary user emulation in cognitive radio networks. [ABSTRACT FROM AUTHOR]

Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN. [ABSTRACT FROM AUTHOR]

Cyber-security threats and issues have been exponentially increasing over the last two decades, including in cognitive radio networks. These attacks and vulnerabilities negatively impact the normal functioning of these networks. Investigating these vulnerabilities and their countermeasures is a necessary step toward securing the cognitive radio based networks. Although there have numerous survey papers on cognitive radio security, a few papers related to the security issues over the physical layer which has a primordial role in establishing radio communications. Thus, an up to date overview of the ongoing research about the threats, detection, and countermeasures techniques over the physical layer is necessary. In this paper, different attacks targeting the physical layer are described and analyzed. Detection methods and countermeasures corresponding to each attack are also discussed and compared. Challenges and future directions are presented as well. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio network (CRN) is a promising technology, which enables secondary users to use the free spectrum channels without causing detrimental interference with the primary user (PU). Nevertheless, CRN is subject to numerous cyber attacks that have a negative impact on its performance. Among the CRN attacks, the primary user emulation (PUE) attack is known to be one of the malicious attacks threatening CRN security. Several attacks detection techniques, based on attacker localization, have been investigated in the literature. These techniques include the trilateration, received signal strength indication (RSSI), and network coding approach as well. However, most of these techniques do not consider the uncertainty related to CRN, which can be modeled by a cost function defined as a weighted sum of conditional probabilities. In this paper, a localization technique, relied on a trilateration computation and a Bayesian model, is proposed for PUE position detection purpose under uncertainty conditions assumption. Particularly, the estimation of PUE position is performed through trilateration method based on RSSI at the anchor nodes for the signal coming from either PU or PUE, whereas, the Bayesian decision model, based on a cost function, is involved to check the PU legitimacy. The simulation results show that the decision‐making approach "Security, productivity, Balancing" influences directly the zone of the PUE attack detection. [ABSTRACT FROM AUTHOR]

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc... performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network. [ABSTRACT FROM AUTHOR]